جست‌وجوی استاد و پژوهشگر

Yanchao Liuمشاهده پروفایل

دانشیار

• Industrial Engineering
• Operations Research
• Transportation Systems Optimization
• +۴ مورد دیگر

Dr. Yanchao Liu is an Associate Professor at Wayne State University's College of Engineering, Department of Industrial and Systems Engineering. He has received research funding from the National Science Foundation and the State of Michigan, including the NSF Career Award. His academic career spans prior industry roles as a Data Scientist and Manager of Advanced Analytics at Sears Holdings Corporation (2016-2017) and Director of Brand Marketing Analytics at Catalina Marketing Corporation (2017). He teaches courses in data science, IoT, and stochastic processes. B.S. Industrial Engineering, Huazhong University of Science and Technology (2006) M.S. Industrial Engineering, University of Arkansas (2008) Ph.D. Industrial and Systems Engineering, University of Wisconsin-Madison (2014) Dr. Liu's research focuses on mathematical modeling for transportation systems, industrial AI, and data analytics. His work addresses drone traffic management, battery-constrained delivery routing, and optimization algorithms for urban mobility. He has developed novel methods for UAV safety diagnostics, random forest implementations, and fairness-aware path planning in urban air mobility. His publications span journals like Journal of Guidance, Control and Dynamics , Transportation Research Part C , and IEEE Transactions on Intelligent Transportation Systems , with conference contributions at IISE and FAIM. His research combines theoretical advancements with practical applications in smart cities and logistics. NSF Career Award (2020) Faculty Research Excellence Award (2021) IEEE PES Best Conference Paper (2015) IEEE Transactions on Smart Grid Best Reviewer (2015) Hubei Province Distinguished Bachelor’s Thesis Award (2006) Dr. Liu advises PhD students like Zhenyu Zhou and J. Chen. He has contributed to energy market modeling (with M.C. Ferris) and published extensively on drone operations, machine learning algorithms, and stochastic processes. His work includes U.S. patent pending applications for UAV safety systems.

Noah Molotchمشاهده پروفایل

استاد

• Snow Hydrology
• Surface Water Hydrology
• Remote Sensing
• +۵ مورد دیگر

Noah Molotch is a Professor of Geography at the University of Colorado Boulder, where he serves as Director of the Mountain Hydrology Group and is a Fellow of INSTAAR (Institute of Arctic and Alpine Research). His research and teaching focus on hydrologic processes in mountainous regions with expertise in snow hydrology, remote sensing, and ecohydrology. Molotch earned his Ph.D. from The University of Arizona in 2004. His research utilizes ground-based observations, remote sensing, and computational modeling to understand hydrological processes, particularly snow distribution and water-carbon-nitrogen fluxes in mountain ecosystems. His work has significant implications for sustainable resource management and environmental policy. His recent publications show a strong focus on snow water equivalent estimation, snowmelt timing impacts on forest productivity, and the development of advanced remote sensing techniques for monitoring mountain hydrology. His research increasingly addresses climate change impacts on water resources, with particular attention to the Western United States. Fellow of INSTAAR Art+Science fellowship in partnership with artist Hannah Taylor Molotch advises numerous graduate students and leads significant research projects including those funded by NASA and NSF. His Mountain Hydrology Group operates field sites across the Western U.S., including Storm Peak Laboratory and Niwot Ridge Long Term Ecological Research Program. The group produces near-real-time snow water equivalent estimates and contributes to the Snow Today website at the National Snow and Ice Data Center.

Heesung Wooمشاهده پروفایل

استادیار

• Robotics in Forestry
• Sensor Integration
• Precision Forestry
• +۴ مورد دیگر

Dr. Heesung Woo is an Assistant Professor of Advanced Forestry at the College of Forestry, Oregon State University , specializing in robotics, sensor integration, and precision forestry. His work focuses on autonomous forestry machinery, AI-driven forest management, and sustainable practices. He advises two graduate students and collaborates internationally through research projects. Research Interests: Autonomous Forest Machinery Development Sensor Integration & ICT Solutions Precision Forestry via Remote Sensing/LiDAR/GIS Machine Learning for Forest Inventory Advanced Forestry Practices for Sustainability Publications emphasize innovative applications of technology in forestry, including LIDAR integration, harvester data analytics, and carbon offset project modeling. His work bridges engineering, environmental science, and policy. Dr. Woo leads the Advanced Forestry Lab at Oregon State, focusing on real-world deployment of cutting-edge technologies to address challenges in forest operations, sustainability, and resource optimization.

Ana Inés Torresمشاهده پروفایل

دانشیار

• Process Systems Engineering
• Sustainability
• Decarbonization
• +۵ مورد دیگر

Ana Inés Torres is an Associate Professor in the Department of Chemical Engineering at Carnegie Mellon University's College of Engineering. She leads an active research group focused on sustainable process systems engineering, with affiliations at the Center for Advanced Process Decision-Making and the Wilton E. Scott Institute for Energy Innovation. Her work bridges chemical engineering with sustainability challenges, particularly in decarbonization and circular economy applications. Dr. Torres earned her educational credentials from Universidad de la República Oriental del Uruguay and the University of Minnesota: Ph.D. in Chemical Engineering, University of Minnesota (2013) Diploma in Chemical Engineering, Universidad de la República Oriental del Uruguay (2005) B.S. in Chemistry, Universidad de la República Oriental del Uruguay (2003) Her research interests span process systems engineering with a sustainability focus, particularly in chemical industry decarbonization through electrification and biomass utilization, circular economy network analysis, and environmentally-friendly rare earth element recovery processes. She integrates modeling, analysis, and optimization to design clean and sustainable chemical processes, with growing emphasis on machine learning applications in process optimization. Analyzing her recent publications reveals a strong focus on decarbonization strategies for existing industrial infrastructure, particularly oil refineries, and circular economy network design. Her work demonstrates increasing integration of machine learning with traditional process systems engineering approaches to tackle complex sustainability challenges across multiple scales, from molecular recovery processes to entire supply chain networks. Dr. Torres has received several prestigious recognitions: NSF CAREER award (2024) Dean's Early Career Fellowships award (2025) Consultant for United Nations Industrial Development Organization (UNIDO) (2024) Associate editor of Clean Technologies and Environmental Policy She actively mentors a diverse group of graduate students working on cutting-edge sustainability challenges, with recent projects focusing on circular economy networks, rare earth element recovery, and bio-refinery design. Her research has attracted significant funding, including the NSF CAREER award, and she participates in multiple collaborative initiatives through CMU's energy research centers. Dr. Torres also serves as an invited speaker at major conferences including FOCAPD and FOCAPO/CPC. Dr. Torres leads the Torres Research Group at CMU, which maintains strong connections with industry partners and international organizations including UNIDO. The group operates within CMU's robust energy research ecosystem, collaborating with the Wilton E. Scott Institute for Energy Innovation and the Center for Advanced Process Decision-Making to address complex sustainability challenges through interdisciplinary approaches.

Leila Characterمشاهده پروفایل

استادیار

• Machine Learning
• Geospatial Analysis
• Marine Archaeology
• +۶ مورد دیگر

Leila Character is an Assistant Professor at Texas A&M University, with expertise in machine learning and geospatial analysis. Her research bridges disciplines like archaeology, environmental science, and geospatial intelligence, often involving fieldwork and computational modeling. She has a multidisciplinary background as a professional geologist, environmental scientist, and AI researcher. Ph.D. and M.A. in Geography and the Environment from University of Texas at Austin B.S. in Geology with a minor in Anthropology/Archaeology from Sewanee: The University of the South Her active projects focus on underwater aircraft wreck detection for MIA service members, multimodal sensor fusion with autonomous underwater vehicles, seafloor characterization in West Africa, and ancient burial mound detection in Romania using vegetation indices. All projects emphasize combining computational rigor with field validation. Recent publications highlight deep learning applications in marine archaeology, Maya cave detection, and sensor fusion technologies. These articles span 2019–2025 and reflect cross-cutting themes in AI-driven geospatial analysis, underwater exploration, and archaeological discovery.

Clément Malletمشاهده پروفایل

پژوهشگر

• Geospatial Computer Vision
• Remote Sensing
• Machine Learning
• +۴ مورد دیگر

Clément Mallet is a Senior Researcher and Director of the LASTIG laboratory at Université Gustave Eiffel, IGN, and École Nationale des Sciences Géographiques (ENSG) in Champs-sur-Marne, France. He leads research in geospatial computer vision, focusing on the intersection of remote sensing, computer vision, and machine learning. His responsibilities include overseeing 75 laboratory members and directing the STRUDEL research team focused on spatio-temporal information modeling. Education: Habilitation (HDR) in Geographical Information Science, Université Paris-Est (2016) PhD in Image and Signal Processing, Télécom ParisTech (2010) Engineering Degree in Geographical Information Science, ENSG (2005) Master's in Remote Sensing, Université Paris 6 (2005) Research Interests: Dr. Mallet specializes in multi-modal land-cover mapping, change detection, geohistorical image analysis, and airborne lidar processing. His work integrates deep learning with geospatial data analysis to solve complex problems in environmental monitoring, urban studies, and historical geography. Current research explores foundation models for earth observation and semantic change detection using hybrid data generation techniques. Publication Trends: Mallet's recent articles (2021-2025) demonstrate strong focus on deep learning applications for geospatial challenges: 40% address land-cover mapping innovations, 30% develop novel change detection methodologies, 20% advance lidar data processing, and 10% explore historical map analysis. His work consistently bridges computer vision theory with operational remote sensing applications. Awards and Recognition: Schwidefsky Medal from ISPRS (2016) 5x Outstanding Reviewer awards (CVPR/ECCV/ICCV 2017-2024) Best Paper Awards at GEOBIA 2016 and ISPRS 2014 Young Researcher Award from GDR ISIS (2010) EuroSDR Best PhD Thesis supervision (2020) Research Leadership: Directs multiple national and international projects including MAESTRIA (ANR-funded multi-modal EO analysis) and HIATUS (historical image analysis). Supervised 14+ PhD students in geospatial AI topics. Secured funding from ANR, CNES, EU H2020 (VOLTA, LandSense), and industrial partners. Leads the STRUDEL team developing cutting-edge methods for territory dynamics analysis. Professional Service: Editor-in-Chief of ISPRS Journal of Photogrammetry and Remote Sensing (2021-present). Organized major conferences including ISPRS Congress (2020-2022 Program Chair) and JURSE events. Active in ISPRS working groups since 2008, currently leading initiatives in large-scale machine learning applications for geospatial data.

Yevgeny Seldinمشاهده پروفایل

استاد

• Machine Learning
• Online Learning
• Bandits
• +۴ مورد دیگر

Yevgeny Seldin is a Professor in the Department of Computer Science at the University of Copenhagen, specializing in Machine Learning Theory . He leads the Machine Learning Section and is a member of the DeLTA Lab . Education : PhD in Computer Science at The Hebrew University of Jerusalem under supervision of Prof. Naftali Tishby His research focuses on Machine Learning , particularly Online Learning and PAC-Bayesian Analysis , with applications to Bandit Algorithms , Reinforcement Learning , and Information Theory . Recent work includes optimal algorithms for delayed feedback, stochastic-adversarial trade-offs, and feedback graphs. Positions Available : PhD and Postdoc positions in Theoretical Machine Learning or energy sector applications Labs & Collaborations : Head of Machine Learning Section Member of DeLTA Lab

Kishalay Mitraمشاهده پروفایل

استاد

• Optimal Control Under Uncertainty
• Bioenergy Supply Chain Design
• Surrogate Multi-Objective Optimization
• +۱۳ مورد دیگر

Kishalay Mitra is a Professor at the Indian Institute of Technology Hyderabad , with affiliations to the Department of Chemical Engineering , Department of Climate Change , and Department of Artificial Intelligence . He also holds visiting professorships at Washington University in St. Louis and University of Washington, Seattle . His work in the Global Optimization & Knowledge Unearthing Laboratory (GOKUL) spans interdisciplinary optimization, machine learning, and their applications in industrial-scale engineering problems. Education : Ph.D. from IIT Bombay. Research Interests : Mitra's research focuses on optimization under uncertainty , surrogate modeling , multi-objective optimization , and integrating machine learning with physics-based models . His work addresses real-world challenges in wind energy , bioenergy supply chains , chemical process control , nanoscience , and environmental modeling (e.g., PM10 spatiotemporal analysis, forest fire prediction, and carbon capture). Article Trends : His recent publications emphasize wind energy systems (layout optimization, yaw control, forecasting), materials science (precipitate growth prediction, polymerization), and industrial processes (crystallization, grinding circuits). Techniques include neural operators , Bayesian optimization , generative adversarial networks (GANs) , and explainable AI .

Jaana Kaarina Bäckمشاهده پروفایل

استاد

• Forest Ecology
• Atmospheric Sciences
• Plant Ecophysiology
• +۴ مورد دیگر

Professor Jaana Kaarina Bäck is affiliated with the University of Helsinki, serving in the Faculty of Agriculture and Forestry's Department of Forest Sciences. She leads the 'Ecosystem processes' research group and oversees ecosystem research at the SMEAR measurement network. Her work bridges forest ecology, atmospheric science, and climate change mitigation, focusing on forest-atmosphere interactions and biogenic volatile organic compounds (BVOCs). PhD in Physiological Plant Ecology (University of Oulu, 1994) Docentship in Forest-Atmosphere Interactions (University of Helsinki, 2010) Docentship in Plant Ecophysiology (University of Oulu, 1998) Bäck's research explores how boreal forests influence atmospheric composition, carbon cycling, and climate dynamics. She investigates BVOC emissions, ecosystem responses to climate change, and feedback mechanisms between vegetation and the atmosphere. Her recent publications (277 total) analyze carbon dynamics after forest thinning, aerosol production in boreal ecosystems, and harmonized environmental observations via eLTER. These works span soil functions, air quality, and climate risk research. Knight, First Class, Order of the White Rose of Finland (2021) Open Science Award (University of Helsinki, 2018) Pro Scientia Award (Finnish Academy of Sciences and Letters, 2017) Bäck supervises 3 postdoctoral researchers, 4 doctoral students as main supervisor, and 12 as co-supervisor. She has secured over 6 M€ in research funding, including EU Horizon grants and Academy of Finland projects.

Brendan Choatمشاهده پروفایل

استاد

• Plant hydraulics
• Xylem function
• Drought stress responses
• +۶ مورد دیگر

Professor Brendan Choat is a leading plant physiologist and Professor at the Hawkesbury Institute for the Environment, Western Sydney University. With a distinguished career in plant hydraulics and water relations research, he has established himself as a global expert in understanding how plants respond to drought stress. His work spans both natural ecosystems and agricultural systems, with particular emphasis on Australian native forests and crop species. Choat's research focuses on the intricate relationship between plant water transport systems and environmental stressors, particularly drought. His work examines how the xylem tissue functions as a hydraulic system that must balance water delivery to leaves while avoiding cavitation (embolism) that can lead to plant mortality. His groundbreaking research has demonstrated that many woody plant species operate close to their physiological safety margins with respect to drought, making them vulnerable to future climate changes. His laboratory employs cutting-edge non-invasive imaging techniques, including X-ray Micro Computed Tomography (microCT) and Magnetic Resonance Imaging (MRI), to directly visualize xylem function in living plants. This approach has allowed his team to address fundamental questions about how cavitation forms and spreads through plant vascular systems during drought stress. Analysis of Professor Choat's extensive publication record reveals a consistent focus on plant drought responses, with particular emphasis on Eucalyptus species and mangrove ecosystems. His research has increasingly incorporated large-scale monitoring approaches, remote sensing data, and trait databases to understand vegetation responses to climate extremes across broader spatial scales. Clarivate Highly Cited Researcher (2018-2024) ARC Future Fellowship (2013) Humboldt Fellowship for Experienced Researchers (2010) Thomson Reuters Citation and Innovation Award (2015) Professor Choat leads multiple significant research projects examining tree dieback in Australian forests, particularly focusing on Eucalyptus species. His work with citizen scientists through the 'Dead Tree Detective' project has provided valuable data on drought impacts across diverse forest biomes. He maintains active collaborations with researchers across Australia and internationally, contributing to large-scale initiatives like the AusTraits plant trait database. His research has direct implications for forest management, conservation strategies, and predicting ecosystem responses to climate change.

Dominik Roeserمشاهده پروفایل

استاد

• Sustainable Forest Management
• Forest Bioproduction and Bioeconomy
• Forest Operations Technology
• +۴ مورد دیگر

Dominik Roeser is a Professor and Associate Dean of Research Forests & Community Outreach at the University of British Columbia's Faculty of Forestry, Department of Forest Resources Management. With over 21 years of experience in forest research and innovation, he has built a comprehensive forest operations research program since joining UBC in 2018, following his tenure as Senior Director at FPInnovations where he managed multidisciplinary teams focused on improving forest sector competitiveness and wildfire management solutions in Western Canada. Professor Roeser's research interests center on sustainable forest management and the bioeconomy, with specific expertise in forest bioproduction, supply chain design, steep slope harvesting, and biomass operations. His work through the Forest Action Lab applies diverse research methods including productivity studies, field trials, and modeling to address sustainability challenges across different operational environments. His research portfolio spans sustainable forest biomass utilization, harvesting in difficult terrain, innovative forest planning tools, operational productivity, carbon management, and community sustainability impacts from reforestation. His publication record shows a strong focus on practical applications of forest science, with recent work emphasizing wildfire management, remote sensing technologies for precision forestry, biomass energy systems, and the socio-ecological dimensions of forest management. His research increasingly integrates advanced technologies like LiDAR and drone-based systems with traditional forest operations to address contemporary challenges in sustainable forest management. Roeser has received the Recognition Award from the Canadian Forest Service (2017) for his contributions to forest science and innovation. His work demonstrates significant impact on both academic understanding and practical implementation of sustainable forest operations across North America and Europe. As an educator, Professor Roeser teaches several key courses including FOPR 264 Introduction to Forest Operations, FOPR 362 Harvesting systems and forest access, FOPR 464 Operational planning and management, and FRST 452 Coastal field school. He considers educating the next generation of forestry professionals one of his passions, bridging theoretical knowledge with practical industry applications. The Forest Action Lab, led by Professor Roeser, represents a multidisciplinary research hub applying diverse methodologies to address forestry stakeholders' needs across British Columbia, Canada, and globally. The lab's work connects academic research with industry implementation, focusing on practical solutions for sustainable forest utilization in varied operational environments.

Banu Lokmanمشاهده پروفایل

استاد

• Operational Research
• Multi-criteria Decision Making
• Optimization
• +۳ مورد دیگر

Banu Lokman is a Professor of Operational Research (OR) at the University of Portsmouth, serving as Associate Head (Research and Innovation) in the School of Organisations, Systems and People within the Faculty of Business & Law. She leads the Centre for Innovative and Sustainable Finance and contributes to the Centre for Operational Research & Logistics. Her expertise spans multi-criteria decision-making, optimization, and their applications in healthcare and sustainability. She holds editorial roles at OMEGA and the IMA Journal of Management Mathematics and organizes the NATCOR MCDM courses. Previously, she served as Deputy Director of CORL (2011–2024), Secretary of the International MCDM Society, and Board Member of INFORMS MCDM Section. She currently chairs the INFORMS MCDM Section as President-elect/Vice-President. Education: BSc, MSc, and PhD in Industrial Engineering from Middle East Technical University (METU, Turkey), followed by postdoctoral research at Aalto University (Finland). She taught at METU (2014–2019) and held visiting roles at Aalto University. Research Interests: Focuses on developing optimization methods for multi-criteria decision problems, particularly in healthcare (e.g., optimizing prostate biopsy decisions with Portsmouth NHS Trust) and sustainability. Her work emphasizes algorithms for nondominated set representation, robust efficiency analysis, and cluster ensemble methods. Key Awards: Bernard Roy Award (2022) for outstanding contributions to Multiple Criteria Decision Aiding, and Young Researcher Award (2015). Advising & Grants: Leads a healthcare-related PhD project and contributes to projects like the Social Investment Fund collaboration with Waltham Forest Council. She actively supervises students and participates in research initiatives on supply networks and data control systems. Labs & Teams: Engaged with interdisciplinary teams in operational research and logistics, particularly in applying OR to real-world challenges such as energy market optimization and MRO supply networks.

Abdulkadir C. Yucelمشاهده پروفایل

استادیار

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +۷ مورد دیگر

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

Maneesh Agrawalaمشاهده پروفایل

استاد

• Computer Graphics
• Human-Computer Interaction
• Information Visualization
• +۲ مورد دیگر

Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and Director of the Brown Institute for Media Innovation. His research spans computer graphics, human-computer interaction, and information visualization. Agrawala's lab develops computational tools for visual communication, investigating how design principles improve media effectiveness. Current projects include diffusion models for image and video generation, sketch-based interfaces, and visualization tools for scriptwriting. His team creates systems that enable new forms of content creation and analysis. His publications demonstrate consistent innovation in visual computing, with recent advances in controllable generative models, video understanding, and visualization design. Agrawala has received numerous honors including the MacArthur Fellowship and ACM Fellowship for his contributions to visual computing.

Sunny K. Boydمشاهده پروفایل

استاد

• Behavioral Neuroendocrinology
• Neuroscience & Behavior
• Animal Behavior
• +۱۳ مورد دیگر

Sunny K. Boyd is a Professor in the Department of Biological Sciences at the University of Notre Dame, where she has served since 1987. She also held significant administrative roles, including Associate Vice President for Research (2009–2013) and Director of Graduate Studies (2008–2013). Her research integrates neuroscience, endocrinology, and computational modeling to understand the chemical regulation of animal behavior. Her educational background includes: A.B. in Biology, Princeton University (1981) M.S. in Behavioral Endocrinology, Oregon State University (1984) Ph.D. in Neuroendocrinology, Oregon State University (1987) Dr. Boyd’s research focuses on the interactions of neuropeptides, neurotransmitters, and neurosteroids in controlling social behaviors, particularly in amphibians. Her lab employs a multi-level approach—spanning molecular, cellular, organismal, and computational levels—to investigate mechanisms from synapses to social interactions. Key interests include GABAergic signaling, neurosteroid modulation, and the role of vasotocin in vocal and reproductive behaviors. Her recent publications reflect a strong trend in integrating computational models with empirical data, especially using agent-based simulations to explore mate choice, aggression, and spatial behavior. These studies bridge neuroscience and behavioral ecology, offering insights into both animal and potential human social behaviors. She has received notable scientific recognition, including: Joyce Award for Excellence in Undergraduate Teaching (2007, 1999) Kaneb Faculty Fellow (2003–2004) Editorial roles for General and Comparative Endocrinology and Hormones and Behavior Service on NSF, NIH, and American Heart Association review panels Dr. Boyd is actively involved in mentoring and training students at all levels. She has advised multiple graduate students and currently leads a vibrant research team. Her lab is supported by grants from the National Science Foundation (NSF), National Institutes of Health (NIH), and U.S. Forest Service. She emphasizes interdisciplinary collaboration, notably with computer scientists on modeling projects. She also teaches courses in neuroscience, physiology, and scientific writing, with a focus on training future health professionals. The Boyd Lab operates at the intersection of experimental and synthetic approaches, combining wet-lab neuroscience with computational modeling. Current projects include: Peptide control of vocalization in amphibians GABA and neurosteroid interactions in neural circuits Agent-based modeling of mate choice and social behavior The lab fosters collaboration across biology, psychology, engineering, and computer science, maintaining an interdisciplinary and innovative research environment.